Social networks of lexical innovation

Packages

Case studies

Load data

For one lemma

Comparative analyses

Check tweets

tweets %>%
  select(tweet) %>%
  slice(., sample(1:n()))

Usage intensity

uses <- get_uses(tweets)
uses_tot <- get_uses_tot(uses)
age = get_age(uses)
coef_var <- get_coef_var(uses)
mean_date <- get_mean_date(uses)
max_date <- get_max_date(uses)
uses_month <- conv_uses_month(uses)
uses_plt <- plt_uses(uses_month, lemma, mean_date, max_date)
uses_plt

Advanced (S-curve)

Candidates

• ghosting
• deep learning
• artificial intelligence
• co-working
• climate emergency

Degree centralization

Diachronic

df_comp %>%
  filter(LEMMA ==lemma) %>%
  select(SUBSET, CENT_DEGREE) %>%
  mutate(SUBSET = factor(SUBSET, levels=c('first', 'mean', 'max', 'last', 'full'))) %>%
  ggplot(., aes(x=SUBSET, y=CENT_DEGREE)) +
    geom_point()

Comparative analyses

Processing status

Lemma list

df_comp %>%
  select(LEMMA, SUBSET, STAMP) %>%
  filter(SUBSET == 'full') %>%
  mutate(STAMP = as_datetime(STAMP)) %>%
  arrange(desc(STAMP))

Dataset statistics

df_comp %>%
  filter(SUBSET == 'full') %>%
  select(LEMMA, SUBSET, USES, USERS) %>%
  dplyr::summarise(
    USES_TOT = sum(USES),
    USERS_TOT = sum(USERS)
    )

Degree centrality

Overall

List

df_comp %>%
  select(LEMMA, SUBSET, USES, CENT_DEGREE) %>%
  filter(SUBSET == 'full') %>%
  # filter(USES > 20000) %>%
  arrange((CENT_DEGREE))

Plot

plt <- df_comp %>%
  select(LEMMA, SUBSET, USES, CENT_DEGREE) %>%
  filter(SUBSET == 'full') %>%
  arrange((CENT_DEGREE)) %>%
  ggplot(., aes(x=CENT_DEGREE, y=reorder(LEMMA, CENT_DEGREE))) +
    geom_point() +
    scale_x_continuous(trans='log')
ggplotly(plt)

Over time

Across all lemmas

Biggest changes

Good examples:

• blockchain

library(tidyr)

df_comp %>%
  select(LEMMA, SUBSET, CENT_DEGREE) %>%
  filter(SUBSET %in% c('first', 'last')) %>%
  dplyr::group_by(LEMMA) %>%
  dplyr::mutate(CENT_DIFF = lag(CENT_DEGREE) - CENT_DEGREE) %>%
  drop_na() %>%
  select(-SUBSET) %>%
  rename(CENT_LAST = CENT_DEGREE) %>%
  arrange(desc(CENT_DIFF))

Usage intensity vs. network characteristics

Uses vs. degree centralization

Plot

plt <- df_comp %>%
  filter(SUBSET == 'full') %>%
  select(LEMMA, CENT_DEGREE, USES, EDGES) %>%
  ggplot(., aes(x=CENT_DEGREE, y=USES)) +
    geom_text(aes(label=LEMMA)) + 
    scale_y_continuous(trans='log') +
    scale_x_continuous(trans='log') +
    geom_smooth(method=lm)
ggplotly(plt)

Correlation

df_corr_full <- df_comp %>%
  filter(
    SUBSET != 'full',
    EDGES >= 100
    ) %>%
  select(-c(LEMMA, SUBSET, NET_WINDOW_DATES, SKIP, STAMP, NROWS))
  
cor.test(df_corr_full$USES, df_corr_full$CENT_DEGREE)

    Pearson's product-moment correlation

data:  df_corr_full$USES and df_corr_full$CENT_DEGREE
t = -2.0737, df = 337, p-value = 0.03887
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.216180217 -0.005795898
sample estimates:
       cor 
-0.1122458 

Degree centrality vs. communities

Correlation

df_comp %>%
  filter(SUBSET == 'last') %>%
  select(CENT_DEGREE, COMMUNITIES) %>%
  mutate(COMMUNITIES = as.numeric(COMMUNITIES)) %>%
  correlate()

Plot

df_comp %>%
  filter(SUBSET == 'last') %>%
  select(LEMMA, CENT_DEGREE, COMMUNITIES) %>%
  ggplot(., aes(x=CENT_DEGREE, y=as.numeric(COMMUNITIES))) +
    geom_text(aes(label=LEMMA)) +
    scale_x_continuous(trans='log')

Uses vs. users

Plot

plt <- df_comp %>%
  filter(SUBSET == 'full') %>%
  select(LEMMA, USES, USERS) %>%
  ggplot(., aes(x=USERS, y=USES)) +
    geom_text(aes(label=LEMMA)) +
    scale_x_continuous(trans='log') +
    scale_y_continuous(trans='log') +
    geom_smooth(method=lm)
ggplotly(plt)

Correlation

df_comp %>%
  filter(SUBSET == 'full') %>%
  select(USES, USERS) %>%
  correlate()

Coefficient of variation

df_comp %>%
  filter(SUBSET == 'full') %>%
  select(LEMMA, COEF_VAR) %>%
  arrange(desc(COEF_VAR))

COEF_VAR vs. CENT

df_comp %>%
  filter(SUBSET == 'full') %>%
  select(LEMMA, COEF_VAR, CENT_DEGREE) %>%
  ggplot(., aes(y=COEF_VAR, x=CENT_DEGREE)) +
    geom_text(aes(label=LEMMA)) +
    scale_y_continuous(trans='log')

    # geom_smooth(method=lm)

Correlations: EDA

library(Hmisc)

df_corr <- df_comp %>%
  # filter(SUBSET == 'last') %>%
  select(-c(LEMMA, SUBSET, NET_WINDOW_DATES, SKIP, STAMP, NROWS))
  # select(-c(USERS, AGE)) %>%
  # mutate(FOCUS = USES) %>%
  # focus(FOCUS) %>%
  # ggplot(., aes(reorder(rowname, FOCUS), FOCUS)) +
    # geom_col() +
    # coord_flip()
  # rearrange() %>%
  # shave() %>%
  # rplot()
  # network_plot(min_cor=.5) %>%